code: def test_predict_skips_profiling_when_specified(
      tmp_recipe_root_path: Path,
      predict_step_output_dir: Path,
  ):
      model_name = "model_" + get_random_id()
      model_uri = train_log_and_register_model(
          model_name, is_dummy=True
      )
      with mock.patch(
          "mlflow.recipes.utils.step.get_pandas_data_profiles"
      ) as mock_profiling:
          recipe_config = read_yaml(
              tmp_recipe_root_path, _RECIPE_CONFIG_FILE_NAME
          )
          recipe_config.update(
              {
                  "steps": {
                      "predict": {
                          "output": {
                              "using": "parquet",
                              "location": str(
                                  predict_step_output_dir.joinpath(
                                      "output.parquet"
                                  )
                              ),
                          },
                          "model_uri": model_uri,
                          "skip_data_profiling": True,
                      },
                  },
              }
          )
          predict_step = PredictStep.from_recipe_config(
              recipe_config,
              str(tmp_recipe_root_path),
          )
          predict_step.run(str(predict_step_output_dir))
  
      expected_step_card_path = os.path.join(
          str(predict_step_output_dir), "card.html"
      )
      with open(expected_step_card_path) as f:
          step_card_html_content = f.read()
      assert "Profile of Scored Dataset" not in step_card_html_content
      mock_profiling.assert_not_called()
  
imports: import os
  import tempfile
  from pathlib import Path
  from unittest import mock
  import pandas
  import pytest
  from pyspark.sql import SparkSession
  from sklearn.datasets import load_diabetes
  import mlflow
  from mlflow.exceptions import MlflowException
  from mlflow.recipes.artifacts import RegisteredModelVersionInfo
  from mlflow.recipes.steps.predict import _INPUT_FILE_NAME, _SCORED_OUTPUT_FILE_NAME, PredictStep
  from mlflow.recipes.steps.register import _REGISTERED_MV_INFO_FILE
  from mlflow.recipes.utils import _RECIPE_CONFIG_FILE_NAME
  from mlflow.utils.file_utils import read_yaml
  from tests.recipes.helper_functions import get_random_id, train_and_log_model, train_log_and_register_model
label_desc: Use image node_id 13 for calling a global function with example usage: test_predict_skips_profiling_when_specified(tmp_recipe_root_path, predict_step_output_dir) without return types